What is the purpose of adversarial testing in AI?

• A. Identify and mitigate biases and vulnerabilities in AI models
• B. Improve training speed and throughput
• C. Reduce data requirements for training
• D. Increase model size to improve capacity

Answer: (A)

Adversarial testing probes how AI models behave when faced with inputs designed to provoke weaknesses. By deliberately crafting challenging, perturbed, or biased inputs, it reveals biases and vulnerabilities that might not show up under normal testing. The goal is to improve robustness and trust by understanding where the model can fail and why, so defenses can be added, training data can be adjusted, or safeguards implemented. This focus on resilience and security distinguishes it from aims like speeding up training, reducing data needs, or simply increasing model size, which are about performance or capacity rather than exposing and mitigating vulnerabilities.